This module provides an interface to an alphanumeric display module. The current version of this driver supports any alphanumeric LCD module based on the:Hitachi HD44780 DOT MATRIX LCD controller.
标签： module alphanumeric interface provides
上传时间： 2013-12-04
上传用户：himbly
书系统地介绍MATLAB 7.0的混合编程方法和技巧。全书共分为13章。第1章和第2章介绍MATLAB的基础知识，第3章简要介绍MATLAB混合编程，第4章至第9章分别介绍几种典型的混合编程方法，包括C-MEX、MATLAB引擎、MAT数据文件共享、Mideva、Matrix和Add-in。第10章、第11章介绍MATLAB与Delphi和Excel的混合编程。第12章介绍MATLAB COM Builder，第13章以图像处理为例介绍了一个综合应用实例。 本书按混合编程的具体方法进行逻辑编排，自始至终用实例描述，每章着重阐述各种混合编程方法的实质和要点，同时穿插了作者多年使用MATLAB的经验和体会。本书既适合初学者自学，也适用于高级MATLAB用户，可作为高等数学、计算机、电子工程、数值分析、信息工程等课程的教学参考书，也可供上述领域的科研工作者参考。 本书所附光盘内容详尽、实例丰富，包含MATLAB实例的源文件、函数/命令和注解以及程序实例。
上传时间： 2013-12-24
上传用户：一诺88
the text file QMLE contains the quasi maximum likelyhood estimating procedure and performing Information Matrix test for a univariate GARCH(1,1) model
标签： estimating likelyhood performing the
上传时间： 2014-11-22
上传用户：zhenyushaw
常见java数据结构的使用方法，包括Arrays类Collections类HashSet类List类TreeSet类Map类Vector类
上传时间： 2014-02-10
上传用户：qiao8960
These instances, whenmapped to an N-dimensional space, represent a core set that can be used to construct an approximation to theminimumenclosing ball. Solving the SVMlearning problem on these core sets can produce a good approximation solution in very fast speed. For example, the core-vector machine [81] thus produced can learn an SVM for millions of data in seconds.
标签： N-dimensional whenmapped instances represent
上传时间： 2016-11-23
上传用户：lixinxiang
This toolbox was designed as a teaching aid, which matlab is particularly good for since source code is relatively legible and simple to modify. However, it is still reasonably fast if used with the supplied optimiser. However, if you really want to speed things up you should consider compiling the matrix composition routine for H into a mex function. Then again if you really want to speed things up you probably shouldn t be using matlab anyway... Get hold of a dedicated C program once you understand the algorithm.
标签： particularly designed teaching toolbox
上传时间： 2016-11-25
上传用户：hustfanenze
PRINCIPLE: The UVE algorithm detects and eliminates from a PLS model (including from 1 to A components) those variables that do not carry any relevant information to model Y. The criterion used to trace the un-informative variables is the reliability of the regression coefficients: c_j=mean(b_j)/std(b_j), obtained by jackknifing. The cutoff level, below which c_j is considered to be too small, indicating that the variable j should be removed, is estimated using a matrix of random variables.The predictive power of PLS models built on the retained variables only is evaluated over all 1-a dimensions =(yielding RMSECVnew).
标签： from eliminates PRINCIPLE algorithm
上传时间： 2016-11-27
上传用户：凌云御清风
The ISD51_Demo project for the MSC1200 shows how to use the ISD51 In-System-Debugger with flash breakpoints or hardware breakpoints. By default, it is configured for flash breakpoints which allow you to set real-time breakpoints in your software. Using Flash breakpoints has also the benefit that no special handing for the shared interrupt vector is required, since the hardware break registers of the MSC1200 are not used at all.
标签： In-System-Debugger ISD the project
上传时间： 2014-11-18
上传用户：dongqiangqiang
Batch version of the back-propagation algorithm. ％ Given a set of corresponding input-output pairs and an initial network ％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the ％ network with backpropagation. ％ ％ The activation functions must be either linear or tanh. The network ％ architecture is defined by the matrix NetDef consisting of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer. ％
标签： back-propagation corresponding input-output algorithm
上传时间： 2016-12-27
上传用户：exxxds
％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ weight decay. Also pruned (ie. not fully connected) networks can ％ be trained. ％ ％ Given a set of corresponding input-output pairs and an initial ％ network, ％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms) ％ trains the network with the Levenberg-Marquardt method. ％ ％ The activation functions can be either linear or tanh. The ％ network architecture is defined by the matrix NetDef which ％ has two rows. The first row specifies the hidden layer and the ％ second row specifies the output layer.
标签： Levenberg-Marquardt desired network neural
上传时间： 2016-12-27
上传用户：jcljkh
